Systems and methods for monitored social media participation

ABSTRACT

Systems and methods to provide for social media monitoring and employee social media monitoring are described herein. In one example, a method includes a determining a score associated with a likelihood that a post on a social media network is associated with a trigger event. The post is transmitted to a computing device, based at least on the score. A response to the post is received from the computing device. An indication that the response is approved is received. The response is posted to the social media network, based at least on the indication that the response is approved.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/301,537, filed Nov. 21, 2011, which claims the benefit of U.S.Provisional Patent Application No. 61/428,664, filed on Dec. 30, 2010,the benefit of priority of which is claimed hereby. U.S. patentapplication Ser. No. 13/301,537 and U.S. Provisional Patent ApplicationNo. 61/428,664 are incorporated by reference herein in their entirety.

TECHNICAL FIELD

This application relates to systems and methods that provide monitoringservices and more particularly, systems and methods that provide formonitored social media participation.

BACKGROUND

Customers demand more from the products and services than ever before.They insist that the companies they deal with on a regular basis providethem greater and greater levels of information and access. As the paceof society quickens, customers' expectations for instant interactionswith companies require more robust tools for customer service.

SUMMARY

A method for monitored media participation may comprise determining ascore associated with a likelihood that a post on a social media networkis associated with a trigger event. The score may be determined byanalyzing the post for keywords, such as using simple string searching,latent semantic analysis, or probabilistic latent semantic analysis. Thescore may be determined, for example, using an algorithm, such as asentiment algorithm, a classification algorithm, a clustering algorithm,or a recommendation algorithm. Based at least on the score, the post maybe transmitted. The post may be transmitted to a computing device. Aresponse to the post may be received, such as from the computing device.An indication that the response to the post is approved may be received,such as from the computing device or from another computing device.Based at least on the indication that the response is approved, theresponse may be posted to the social media network. As another example,a method for monitored media participation may comprise determiningscores indicative of a likelihood that a post on a social media networkis associated with each of a plurality of trigger events. Based at leaston the scores, the post may be transmitted to a recipient associatedwith one of the plurality of trigger events. A response to the post maybe received, such as from the recipient. The response may be posted tothe social media network.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example and not limitation in thefigures of the accompanying drawings, in which like references indicatesimilar elements and in which:

FIG. 1 shows a high-level block diagram of a system for providing socialmedia monitoring services, in accordance with an example embodiment;

FIGS. 2A-2B show high level block diagrams of apparatus for social mediastream monitoring, in accordance with example embodiments;

FIGS. 3A-3B show high level block diagrams of apparatus for employeesocial media interaction monitoring, in accordance with exampleembodiments;

FIG. 4 shows a flowchart of a method of social media lead generation, inaccordance with an example embodiment;

FIG. 5 shows a flowchart of a method of employee social mediainteraction monitoring, in accordance with an example embodiment; and

FIG. 6 shows a block diagram of a machine including instructions toperform any one or more of the methodologies described herein.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

In the following detailed description of example embodiments, referenceis made to the accompanying drawings, which form a part hereof and inwhich is shown, by way of illustration, specific embodiments in whichthe example method, apparatus, and system may be practiced. It is to beunderstood that other embodiments may be utilized and structural changesmay he made without departing from the scope of this description.

Social networks refer to social structures made up of individuals (ororganizations) called “nodes”, which are tied (connected) by one or morespecific types of interdependency, such as friendship, kinship, commoninterest, and the like. As used herein, “social networks” reform tothose interconnections between users of a computer-implemented network,such as the Internet Examples of social networks include, withoutlimitation, FACEBOOK™, TWITTER™, MYSPACE™ WIKIPEDIA™ LINKEDIN™ YOUTUBE™,FLICKR™, WORDPRESS™ blogs, BLOGSPOT™ Blogs, and the like.

The systems and methods described herein relate to monitoring activitieswithin the social media space. In an embodiment, social media is a termused to describe the variety of social networks on which users interactwith each other. Social media is media used for social interaction,which use highly accessible and scalable publishing techniques. Socialmedia allows for the creation and exchange of user-generated content(UGC). Social media represents the blending of technology and socialinteraction for the co-creation of value. Social media differs fromtraditional media (such as newspapers, television, and the like) in itsrelatively low speaking cost (e.g., a “tweet” costs nothing, while evena local cable access TV show costs hundreds or even thousands of dollarsto produce) and its accessibility. This allows all customers to have avoice, and it is becoming more imperative that business begin listeningto this voice.

FIG. 1 shows a high-level block diagram of a system 100 for providingsocial media monitoring services of social media postings, in accordancewith an example embodiment In an embodiment, a social media postingincludes content (e.g., a string of alphanumeric characters, a picture,a movie, a song, etc.) that may be shared among a plurality of users ofa social media community.

In an embodiment, system 100 includes social media portal 102 andincoming social media streams 104 a, 104 b. Social media streams 104 a,104 b are retrieved from a plurality of social media communities, suchas TWITTER™ 106 or FACEBOOK™ 108. In an embodiment, social media streams104 a, 104 b are an aggregation of individual social media postings,such as individual tweets 110 or wall postings 112 that have been postedby users 114 a, 114 b using computing devices 115 a, 115 b. Computingdevices 115 a, 115 b may include, but are not limited to, personalcomputers, personal digital assistants, mobile phones, tablet devices,and laptop computers.

In an embodiment, social media portal 102 is configured to receivesocial media streams 104 a, 104 b of social media postings and performanalysis operations on the social media postings. In an exampleembodiment, social media portal 102 is configured to access (e.g.,though a network using an Application Programming Interface (API) call)social media streams 104 a, 104 b and replicate those streams to aninternally managed database 116. In various embodiments, database 116may retain a certain period of postings from the social media streams104 a, 104 b. In an embodiment, social media streams 104 a, 104 b arefiltered according to defined criteria. The criteria may include anycombination of subject matter (e.g., keywords), demographics (e.g., age,gender, occupation), temporal restrictions (e.g., within the past twoweeks), and geographic restrictions (e.g., city, state, zip code, a fivemile radius). For example, one set of criteria may filter social mediapostings by posts that originate from Texas, are affiliated with themilitary, and are from users of ages 20-25. The database 116 may beimplemented in a number of ways including, but not limited to flat fileand relational databases. In various embodiments, other types ofpersistence storage are used.

In an embodiment, analysis includes performing textual analysis on thecontent of a social media posting to determine a context of the socialmedia posting. In an embodiment, social media portal 102 classifies asocial media posting according to a plurality of trigger events. Forexample, trigger event categories may include life events, complaintevents, and insurance events.

In an embodiment, the trigger events generate one or more leads that maybe handled by employees of the operator of social media portal 102. Forexample, a sales lead may he generated by a trigger event based ondetecting that a user is unhappy with his or her current insurancecarrier. In another example, a customer service lead may be generatedfor complaint event triggers when it is determined the complaint isabout the operator of social media portal 102. In an embodiment, claimleads are generated for insurance events. The leads may be routed to anemployee of the operator of social media portal 102 based on the triggerevent. The employee may then take a number of actions depending on thelead. For example, the employee may send a response social media postingwhen the lead is a customer service lead.

In an embodiment, social media portal 102 is configured to enable one ormore employees 118 to post social media postings to the social networksthrough a single point of entry. In such an example, employee 118 isemployed by the same entity operating the social media portal 102.Alternatively, social media portal 102 may be operated by a third partyproviding monitoring services. In either case, it may be advantageousfor the employer of the employees 118 to be apprised of the employees118 interactions 120 with social networks as those interactions mayinclude statements about the employer that are regulated by one or moregovernmental agencies.

Additionally, it may be advantageous as the employer seeks to maintain aconsistent message about the company, both in traditional media and insocial networks. In an embodiment, each employee 118 interaction 120 ispassed through social media portal 102, in an example, and validatedagainst one or more rules. The interaction 120 is then directed to theappropriate social network. For example, employee 118 tweets 122 aredirected to the TWITTER™ network and become part of the cloud ofTWITTER™ tweets 106; and employee 118 wall postings are directed to theFACEBOOK™ network and become part of the cloud of FACEBOOK™ wallpostings 108.

The interactions between the social networks and social media portal 102may be made by way of application programming interfaces (API). TheseAPIs may be public, in that any software developer may access them andthe documentation describing them is available to all APIs may also beprivate in that access and documentation are provided to those personswho have some contractual relationship with the network operator.Further, access to the networks may be through a combination of publicand private APIs as appropriate. Other access methods are possible,however, and may be used as well to provide additional functionality tosocial media portal 102.

Mention of FACEBOOK™ and TWITTER™ are merely illustrative of two examplesocial networks that are used, but the systems and methods describedherein are not necessarily limited to them, and any social network thatprovides the ability to a user to provide comments and participate in anaggregated conversation about anything is considered to be within thescope of the present invention.

FIG. 2A shows a high level block diagram of an apparatus for socialmedia stream monitoring, in accordance with an example embodiment. In anexample, social media portal (SMP) 200 is configured to receive socialmedia stream 202 and output lead 204. In further examples, social mediaportal 200 may be configured to output additional information asdescribed above. Social media portal 200 is communicatively coupled, inan example, to database 206 that may be used to store social mediapostings. The social media postings may include interactions with one ormore social networks as described above. In an embodiment, database 206is configured to maintain multiple different interactions.

FIG. 2B illustrates a more detailed block diagram of SMP 200 describedabove in FIG. 2A. In an example, SMP 200 includes stream capture engine208, database access engine 210, search engine 212, and scoring engine214.

Stream capture engine 208 is configured, in one example, to accessthrough APIs or other suitable methods, one or more social networkstreams 202. The stream capture engine 208 may include functionality tomonitor the streams of all social networks monitored by the operator ofSMP 200. Alternatively, SMP 200 may include a plurality of streamcapture engines 208 with each engine configured to access a singlesocial network. This may be advantageous for the purposes of loadbalancing as well as scalability.

Database access engine 210, in an example, is communicatively coupled tostream capture engine 208 and database 206. In an embodiment, databaseaccess engine 210 is configured to receive a stream of social mediapostings from stream capture engine 208 and store the social mediapostings in database 206. Additionally, database access engine 210 maybe configured to retrieve information from database 206 or providedatabase query capability to search engine 212 or scoring engine 214.

Search engine 212 is configured to provide searching capability to SMP200. In an embodiment, users (e.g., employees) may search social mediapostings stored in database 206 using a variety of criteria, includingbut not limited to, user, location (e.g., of the user of the socialmedia post or location of the device which sent the social mediaposting), keyword, or trigger event. For keyword searching, searchengine 212 may be completed using any number of suitable text search andparsing methods, including, but not limited to, simple string searching,latent semantic analysis (LSA), probabilistic latent semantic analysis(PLSA), or any other text based analysis method. Additionally, searchengine 212 may be augmented by other search analysis types, such asimage, audio or video search capabilities. These latter types may be ofutility to the operator of the SMP 200 if their customers use sites likeYOUTUBE™ or FLICKR™.

Other machine learning algorithms may also be used to filter searchresults such as classification, clustering, and recommendationalgorithms. These algorithms may be seeded and modified with the help ofusers of the social media portal. For example, through the use of asentiment algorithm the social media posts may be classified as positiveor negative with respect to a company (e.g., whether the post contains acomplaint about the company. A user of the social media portal may viewthe initial results according to the existing sentiment algorithm andindicate whether a post that was classified as negative was, in fact,positive. In this manner, the sentiment algorithm will be more accuratein the future.

In an embodiment, the results of a search are filtered. For example,social media posts which exceed a defined confidence level may be shownin a results page. In an embodiment, the results are formatted asfollows:

<Score, Source, Time, Owner, Contents>

In an embodiment, the score represents the calculated similarity (e.g.,confidence level) between the search criteria and the social mediaposting. The source is the name of the social media site (e.g., socialnetwork such as FACEBOOK™) from which the social media postingoriginated. The time is the time the social media postings were posted(e.g., any combination of year, month, day, hour, minute second, timezone). In an embodiment, the owner is the handle of the account whichposted the social media posting to the social network. In an embodiment,a user may click on the owner's name to retrieve the owner's (e.g.,customer) history of social media postings. In an embodiment, thecontents of the social media posting are included in the search results.

In an embodiment scoring engine 214 scores social media postingsaccording to trigger events. For example, trigger event categories mayinclude life events, complaint events, and insurance events. Life eventsmay include events such as birth of a child, death of a family member,moving to a new house, starting a new job, buying a new car, and beingfired from a job. Complaint events may include events such as slowclaims processing, improper claims processing, and complaints and aboutexisting insurance coverage. Insurance events may include events such asbeing in a car accident, domicile damage, theft, and death of aninsured. The labels of the trigger event categories are exemplary innature and various embodiments may use more or less event triggercategories. In an embodiment, the trigger events are stored in database206 as a series of keywords and business rules that are used todetermine the likelihood of a trigger event. For example, a new cartrigger event may be stored as a rule that says a social media postingthat includes the phrase “I just bought a new car” indicates a highlikelihood of a new car purchase. In various embodiments, a similarityalgorithm is used to determine the likelihood of a trigger event basedon stored phrases and keywords for a trigger event. A social mediaposting may also be scored using similar text search methods or machinelearning algorithm as described with respect to search engine 212.

In various embodiments when trigger events are included in searchresults, a representation (e.g., a qualitative or quantitativemeasurement) of the likelihood of the occurrence of the trigger eventbeing related to the social media posting is displayed concurrently withthe contents of the social media posting and a representation of thetrigger event (e.g., a description of the trigger event) are included inthe search results. In an embodiment, each social media posting isscored according to a set of trigger events as it passes through SMP200. In an embodiment, social media postings are scored for triggerevents when requested by a user of SMP 200.

In an embodiment, lead 204 or the social media posting itself may berouted to a recipient based on the output of scoring engine 214. Forexample, a social media post may be scored for a plurality of triggerevents. In an embodiment, the highest score represents the greatestlikelihood the trigger event is associated with the social mediaposting. Then, a lead may be generated depending on which trigger eventis calculated to have the highest score. For example, a social mediaposting may be scored as 0.3 (e.g., 30% similar) with respect tophrases/keywords associated with for a car accident trigger event and0.5 for a new car trigger event. Because the new car trigger event washigher, a sales lead may be generated. The social media posting may thenbe routed to an employee of the operator of SMP 200 to follow-up.Additional scoring may also be done to further refine the current lead.For example, the post may be scored with respect to sentiment asdescribed above. Then, if the new car trigger event also is scored asnegative, the post may be passed to a customer service representative aswell as the sales department.

In various embodiments, social media postings that have been scored arerecorded as entries in a social media posting queue indicating whichtrigger event the social media posting is associated with. In anembodiment, the calculated likelihood is compared to a threshold valueto determine whether to route the lead or social media posting. Forexample, social media postings that do not have a calculated likelihoodof at least 0.2 may not be routed to anyone.

In various embodiments, routing the social media posting to a recipientincludes presenting the recipient with a user interface for respondingto the social media message. In an embodiment, the user interfaceincludes an option of which social media account to respond from, a textbox for entering a message, an option to make the message private orpublic, a reference to the social media message, and an option toretrieve other social media postings from the poster of the social mediaposting.

FIG. 3A shows a high level block diagram of an apparatus for employeesocial media interaction monitoring, in accordance with an exampleembodiment. The social media portal (SMP) 300 is configured to receiveone or more employee interactions 302 and enable posting of one or moresocial media posts 304 on the appropriate social network. In anembodiment, an interaction is a response social media posting. In anembodiment, response means the posting is a direct response to a socialmedia posting (e.g., a complaint) or response may mean in response to atrigger event or lead. SMP 300 is communicatively coupled to directoryservice 306 and database 308. Directory service 306 providesauthentication and verification services, in an example, to SMP 300. Thedirectory service may include, without limitation, lightweight directoryaccess protocol (LDAP), X.500, eDirectory, Red Hat Directory Server,Open Directory, Apache Directory Server, NT Domains, NetInfo, OpenLDAP,and the like.

FIG. 3B illustrates a more detailed block diagram of social media portal300 described above. In an example, social media portal (SMP) 300includes social network access engine 310, directory service engine 312,database access engine 314, and reporting engine 316. In a furtherexample, reporting engine 316 may be communicatively coupled through asuitable network to a user computer 318 such that the user may monitorthe one or more employee interactions 302 received by SMP 300.

In an example, social network access engine 310 is configured post asocial media posting to a social network on behalf of an employee. Thismay be through APIs as described above, or through other suitablemethods. Similar to stream capture engine 208 described above, socialnetwork access engine 310 may represent a plurality of social networkaccess engines 310 in which each of the plurality are configured to postto a single social network. In various embodiments, social networkaccess engine 310 is configured to comply with regulatory guidelines forposting on social networks. For example, social network access engine310 may add #EMP to any tweets that are sent from employees of theoperator of SMP 300.

In an example, directory service engine 312 is configured to access anavailable directory service 306 to authenticate employees and verify theprivileges of the user. In the context of the SMP 300, the directoryservice engine 312 allows various controls on the interactions ofemployees. For instance, employees in the research and developmentdepartment may be prohibited from posting from work on FACEBOOK™, whilethose employees in customer service are allowed to. In variousembodiments, the privileges include which social media accounts anemployee is authorized to post from. For example, a customer serviceemployee may be authorized to post from an official customer serviceaccount. In various embodiments, employees are authorized to respond tocertain trigger events and leads. For example, a sales employee may beauthorized to respond to sales leads.

Directory service engine 312 may also provide differing retentionprocedures and policies depending on the employee. Employees in highlyregulated industries such as financial planning may have theirinteractions stored in a custom configured data store which providesaudit and reporting capabilities for government regulators.Additionally, those employees may have their interactions maintained ina pending status until a supervisor approves the content. Through thistype of additional structure, an investment company may maintain therigor and control required.

The database access engine 314 is configured, in an example, to receivethe employee interactions (e.g., a response message) and store them indatabase 308. In an embodiment, entries in database 308 are formatted asfollows:

<User, Site, Account, Post, Time>

In an embodiment, “User” is an identification of the employee (e.g., auser id) that was authenticated and initiated the interaction, “Site” isthe social network which the interaction was directed, Account is thesocial media account that was used to post to the social network, “Post”is an identification number of the interaction, and “Time” is the timethe interaction was posted to the social network (any combination ofyear, month, day, hour, minute second, time zone). Additionally, thedatabase access engine 314 may provide query capability to the reportingengine 316 such that users may analyze the postings of employees. In anembodiment, database 308 is an audit database.

The reporting engine 316 is configured to provide, in an example, tousers one or more views, or reports, on the interactions of users,through access by the user computer 318. These views, or reports,provide to the company an audit capability as well as satisfying variousregulations that govern the company's interactions with customers. Inthe United States, these regulations are promulgated by the FederalTrade Commission (FTC). The SMP 300, in one example, is meant to providethe type of monitoring required by the FTC.

Though depicted separately in FIG. 1, FIGS. 2A-2B, and FIGS. 3A-3B, thevarious social media portals described may be combined into a singleplatform, engine or software module, or hardware system such thatcustomer service and employee monitoring are provided for in a singleplace. However, the systems and methods described herein are not limitedto either implementation and are limited only by the claims providedbelow.

FIG. 4 shows a flowchart of a method of social media lead generation, inaccordance with an example embodiment. The operations depicted in FIG. 4may be carried out on the apparatus or systems described above, in someexamples.

At block 405, the system receives a stream of social network postings.This may be accomplished in some examples, by utilizing public orprivate API's provided by the operator of that social network. In otherexamples, this may be accomplished using other data extractionmethodologies such as screen scraping, for example. This stream may bestored in a database for later operations. The database may store acertain period of postings, such as the last 30 days or a longer periodof time. Though it may be possible using these systems to store thesestreams indefinitely, old information may not be as useful as newerinformation, and storing a smaller amount of information may result inincreased system performance. In an embodiment, the social mediapostings are retrieved from a storage device on a social media portal.

The stream of postings may be filtered according to social media postsconcerning an affinity group (e.g., common group of users). For example,the filtering may be done through any variety of means, such as just thefollowers of a company, or the followers of another entity, or thelisted friends, or all of the individuals who indicate they like aparticular topic. To illustrate this, the operator of this system maywish to monitor social network streams related to the US Navy. Theycould choose to capture all of the content posted by people who follow@USNavy on TWITTER™ as well as related TWITTER™ accounts, such as @CVN70(TWITTER™ feed for USS Carl Vinson CVN-70), @AmericanLegion (TWITTER™feed for the American Legion), @DeptofDefense (TWITTER™ feed for the USDepartment of Defense), etc. Through the monitoring of these streams onemay get a sense of the sentiment of these particular users as opposed tothe sentiments of the general population. In various embodiments, thepostings may also be filtered by particular individuals/customers. Inthis manner, a user of SMP may see a history of the social media postsof particular customer.

At block 410, the text of one or more social media postings within thesocial media stream is analyzed. In an embodiment, analysis includesscoring the social media postings for trigger events as described herein(e.g., using scoring engine 214). In various embodiments, analysis maybe looking for the occurrence of the word “baby” in a posting andincrementing the baby counter by one to note how many particular peopleare talking about babies. Using the above example, one could see throughthis analysis if a US Navy supporting company needs to spend more timediscussing their baby-related products. Or a company that specializes inthe relocation of Navy sailors could determine that a specific sailorneeds help in finding a new home following a permanent change of station(PCS) move through that sailor's posting that says “just got my PCSorders and off to San Diego I go.” Though simple string analysis may bequicker, other textual analysis and contextual analysis may be performedto get different results. Methodology such as LSA and PLSA may providemore context and sentiment than just simple text strings, though simpletext strings remain a quick method of performing the analysis of thestream.

At block 415, a social media posting is routed to a recipient based onthe analysis (e.g., scoring) indicating a trigger event. The triggerevent may be associated with one or more leads. In an embodiment, alevel of user access required for responding to a trigger event isdetermined and then the recipient is determined based on a level of useaccess granted to the recipient and the level of access required forresponding to the trigger event. With respect to leads, continuing theexamples above, the first company may decide to start marketing morebaby-centric items. In the second example, that relocation company mayreach out directly to that sailor and tell them about the greatrelocation services they offer.

In various embodiments, after the social media posting is routed to theuser, the SMP may receive an indication that the user responded to thefirst social media posting with a response message. Thereafter, an entrymay be recorded in an audit database indicating the first social mediaposting has been responded to by the user.

FIG. 5 shows a flowchart of a method of employee social mediainteraction monitoring, in accordance with an example embodiment. Theoperations depicted in FIG. 5 may be carried out on the apparatus orsystems described above, in some examples.

At block 505, the system receives an employee interaction. Thisinteraction may be through a dedicated software client on the employee'smachine or through a web interface on the employer's intranet. This typeof entry point may be of particular use when the employee is actuallyusing the company's network, either by being physically at theemployer's building or accessing it through a virtual private network(VPN). Though the employer may promulgate other methods of accessing thesystem, these systems seem to be of particular value in that integrationwith various security mechanisms may occur and the employee may bedirected to these systems and away from publicly available methods, suchas directly interacting with FACEBOOK™.

At block 510, the system accesses a directory service as described aboveto perform one or more authentication and validation exercises. At block515, the employee is validated against that directory service. If theemployee is authorized to post, their posting is written to a databaseat block 520. At block 525, the system effects the posting through anyof the methods already described.

If the employee is not validated by the directory service at block 515,the posting is stored in the database at block 530. The employee may benotified at block 535. Additionally, system administrators may bealerted that an unauthorized employee has attempted to post something toa social network so that appropriate action may be taken.

In addition to the validation operations described here, the systemcould perform any of the textual analysis described above with theintent of determining trends associated with employees as well asemployee sentiment about any variety of topics.

FIG. 6 shows a block diagram of a machine including instructions toperform any one or more of the methodologies described herein. System600 includes computer 610 connected to network 614. Computer 610includes processor 620, storage device 622, output device 624, inputdevice 626, and network interface device 628, all connected via bus 630.Processor 620 represents a central processing unit of any type ofarchitecture, such as a CISC (Complex Instruction Set Computing), RISC(Reduced Instruction Set Computing), VLIW (Very Long Instruction Word),or a hybrid architecture, although any appropriate processor may beused. Processor 620 executes instructions and includes that portion ofcomputer 610 that controls the operation of the entire computer.Although not depicted in FIG. 6, processor 620 typically includes acontrol unit that organizes data and program storage in memory andtransfers data and other information between the various parts ofcomputer 610. Processor 620 receives input data from input device 626and network 614, reads and stores code and data 634 in storage device622, and presents data to output device 624.

Although computer 610 is shown to contain a single processor 620 and asingle bus 630, the disclosed embodiment applies equally to computersthat may have multiple processors and to computers that may havemultiple busses with some or all performing different functions indifferent ways.

Storage device 622 represents one or more mechanisms for storing data.For example, storage device 622 may include read-only memory (ROM),random access memory (RAM), magnetic disk storage media, optical storagemedia, flash memory devices, and/or other non-transitorymachine-readable media. In other embodiments, any appropriate type ofstorage device may be used. Although one storage device 622 is shown,multiple storage devices and multiple types of storage devices may bepresent. Further, although computer 610 is drawn to contain storagedevice 622, storage device 622 may be distributed across othercomputers, for example on a server.

Storage device 622 includes a controller (not shown in FIG. 6) and dataitems 634. The controller includes instructions capable of beingexecuted on processor 620 to carry out the functions, as previouslydescribed above with reference to FIGS. 1-5. In another embodiment, someor all of the functions are carried out via hard ware in lieu of aprocessor-based system. In one embodiment, the controller is a webbrowser, but in other embodiments the controller may be a databasesystem, a file system, an electronic mail system, a media manager, animage manager, or may include any other functions capable of accessingdata items. Of course, storage device 622 may also contain additionalsoftware and data (not shown), which is not necessary to understand theinvention.

Although the controller and the data items 634 are shown to be withinstorage device 622 in computer 610, some or all of them may bedistributed across other systems, for example on a server and accessedvia network 614.

Output device 624 is that part of computer 610 that displays output tothe user. Output device 624 may be a liquid crystal display (LCD). Inembodiments, output device 624 may be a gas or plasma-based flat-paneldisplay or a traditional cathode-ray tube (CRT) display. In otherembodiments, any appropriate display device may be used. Although oneoutput device 624 is shown, in other embodiments any number of outputdevices of different types, or of the same type, may be present. In anembodiment, output device 624 displays a user interface.

Input device 626 may be a keyboard, mouse or other pointing device,trackball, touchpad, touch screen, keypad, microphone, voice recognitiondevice, or any other appropriate mechanism for the user to input data tocomputer 610 and manipulate the user interface previously discussed.Although one input device 626 is shown, in another embodiment any numberand type of input devices may be present.

Network interface device 628 provides connectivity from computer 610 tonetwork 614 through any suitable communications protocol Networkinterface device 628 sends and receives data items from network 614.

Bus 630 may represent one or more busses, e.g., USB (Universal SerialBus), PCI, ISA (Industry Standard Architecture), X-Bus, EISA (ExtendedIndustry Standard Architecture), or any other appropriate bus and/orbridge (also called a bus controller).

Computer 610 may be implemented using any suitable hardware and/orsoftware, such as a personal computer or other electronic computingdevice. Portable computers, laptop or notebook computers, PDAs (PersonalDigital Assistants), pocket computers, appliances, telephones, andmainframe computers are examples of other possible configurations ofcomputer 610. For example, other peripheral devices such as audioadapters or chip programming devices, such as EPROM (ErasableProgrammable Read-Only Memory) programming devices may be used inaddition to, or in place of, the hardware already depicted.

Network 614 may be any suitable network and may support any appropriateprotocol suitable for communication to computer 610. In an embodiment,network 614 may support wireless communications. In another embodiment,network 614 may support hard-wired communications, such as a telephoneline or cable. In another embodiment, network 614 may support theEthernet IEEE (Institute of Electrical and Electronics Engineers) 802.3xspecification. In another embodiment, network 614 may be the Internetand may support IP (Internet Protocol). In another embodiment, network614 may be a local area network (LAN) or a wide area network (WAN). Inanother embodiment, network 614 may be a hotspot service providernetwork. In another embodiment, network 614 may be an intranet. Inanother embodiment, network 614 may be a GPRS (General Packet RadioService) network. In another embodiment, network 614 may be anyappropriate cellular data network or cell-based radio networktechnology. In another embodiment, network 614 may be an IEEE 802.11wireless network. In still another embodiment, network 614 may be anysuitable network or combination of networks. Although one network 614 isshown, in other embodiments any number of networks (of the same ordifferent types) may be present.

The embodiments described herein may be implemented in an operatingenvironment comprising software installed on any programmable device, inhardware, or in a combination of software and hardware.

For the pm1Joses of this specification, the terms “machine-readablemedium” or “computer-readable medium” shall be taken to include anytangible non-transitory medium which is capable of storing or encoding asequence of instructions for execution by the machine and that cause themachine to perform any one of the methodologies described herein. Theterms “machine-readable medium” or “computer-readable medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical or magnetic disks. Further, it will be appreciatedthat the software could be distributed across multiple machines orstorage media, which may include the machine-readable medium.

Method embodiments described herein may be computer-implemented. Someembodiments may include computer-readable media encoded with a computerprogram (e.g., software), which includes instructions operable to causean electronic device to perform methods of various embodiments. Asoftware implementation (or computer-implemented method) may includemicrocode, assembly language code, or a higher-level language code,which further may include computer-readable instructions for performingvarious methods. The code may form portions of computer programproducts. Further, the code may be tangibly stored on one or morevolatile or non-volatile computer-readable media during execution or atother times. These computer-readable media may include, but are notlimited to, bard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAM's), read onlymemories (ROM's), and the like.

In various embodiments, the methods and system described herein may beimplemented on one or more modules or engines. In an embodiment, onemodule or one engine may be implemented as multiple logical modules, orseveral modules may be implemented as a single logical module. Asanother example, modules labeled as “first,” “second,” and “third,”etc., may be implemented in a single module, or in some combination ofmodules, as would he understood by one of ordinary skill in the art.Modules and engines may constitute either software modules (e.g., codeembodied (1) on a non-transitory machine-readable medium or (2) in atransmission signal) or hardware-implemented modules or engines. Ahardware-implemented module or engine is tangible unit capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or moreprocessors may be configured by software (e.g., an application orapplication portion) as a hardware-implemented module or engine thatoperates to perform certain operations as described herein.

Various modifications and changes may be made to the embodimentsdescribed herein without departing from the broader spirit and scope ofthe invention. Accordingly, the specification and drawings are to beregarded in an illustrative rather than a restrictive sense. Theaccompanying drawings that form a part hereof: show by way ofillustration, and not of limitation, specific embodiments in which thesubject matter may be practiced. The embodiments illustrated aredescribed in sufficient detail to enable those skilled in the art topractice the teachings disclosed herein. Other embodiments may beutilized and derived therefrom, such that structural and logicalsubstitutions and changes may be made without departing from the scopeof this disclosure. This Detailed Description, therefore, is not to betaken in a limiting sense, and the scope of various embodiments isdefined only by the appended claims, along with the full range ofequivalents to which such claims are entitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed:
 1. A system comprising: a processor; and memorycomprising computer-readable instructions that, when executed by theprocessor, cause the processor to effectuate operations comprising:monitoring social media stream using a social media portal (SMP) byreceiving the social media stream and an output lead, wherein the socialmedia portal is coupled to a database used for storing social mediaposts, wherein the SMP comprises at least a stream capture engine, adatabase access engine, a search engine, and a scoring engine;determining that a post of the social media posts on a social medianetwork is associated with a trigger event by determining a plurality ofscores associated with the likelihood that a plurality of posts on thesocial media network are associated with the trigger event, wherein thescore represents calculated similarity between search criteria and thesocial media posts, and wherein the trigger event comprises life events,complaint events and insurance events; routing, the social media postsbased on output of the scoring engine, wherein a highest scorerepresents a greatest likelihood the trigger event is associated with asocial media post; generating a lead depending on which trigger event iscalculated to have the highest score; filtering, using classification,clustering and recommendation machine learning algorithms, based atleast on the plurality of scores, the plurality of social media posts;transmitting a request for a response to the filtered plurality ofsocial media posts; transmitting, based at least on the determining thatthe social media post is associated with the trigger event and to acomputing device, the post; receiving, from the computing device, anindication of a social media account; adding the response to a datastore comprising a plurality of responses to the plurality of posts; andposting, based at least on the plurality of responses being approved,the plurality of responses to the social media network under the socialmedia account.
 2. The system of claim 1, wherein the post was identifiedby filtering a social media stream from the social media network.
 3. Thesystem of claim 1, wherein the trigger event comprises at least one of acomplaint, an event associated with insurance, an event associated withpurchase of a vehicle, an event associated with purchase of real estate,or an event associated with a family member.
 4. The system of claim 1,wherein posting the plurality of responses response is further based atleast on an authentication or a verification of a sender of the responseto the post.
 5. The system of claim 1, wherein the determining that thepost is associated with the trigger event comprises determining aplurality of scores associated with a likelihood that the plurality ofsocial media posts on the social media network are associated with thetrigger event; and further comprising filtering, based at least on theplurality of scores, the plurality of posts and transmitting a requestfor the response, wherein the response comprises a response for thefiltered plurality of posts.
 6. The system of claim 5, wherein filteringthe plurality of posts is further based at least on one or more sourcesof the plurality of posts.
 7. A method comprising: monitoring socialmedia stream using a social media portal (SMP) by receiving the socialmedia stream and an output lead, wherein the social media portal iscoupled to a database used for storing social media posts, wherein theSMP comprises at least a stream capture engine, a database accessengine, a search engine, and a scoring engine; determining that a postof the social media posts on a social media network is associated with atrigger event by determining a plurality of scores associated with thelikelihood that a plurality of posts on the social media network areassociated with the trigger event, wherein the score representscalculated similarity between search criteria and the social mediaposts, and wherein the trigger event comprises life events, complaintevents and insurance events; routing, the social media posts based onoutput of the scoring engine, wherein a highest score represents agreatest likelihood the trigger event is associated with a social mediapost; generating a lead depending on which trigger event is calculatedto have the highest score; filtering, using classification, clusteringand recommendation machine learning algorithms, based at least on theplurality of scores, the plurality of social media posts; transmitting arequest for a response to the filtered plurality of social media posts;transmitting, based at least on the determining that the social mediapost is associated with the trigger event and to a computing device, thepost; receiving, from the computing device, an indication of a socialmedia account; adding the response to a data store comprising aplurality of responses to the plurality of posts; and posting, based atleast on the plurality of responses being approved, the plurality ofresponses to the social media network under the social media account. 8.The method of claim 7, wherein the recipient comprises a provider ofservices associated with the one of the plurality of trigger events. 9.The method of claim 7, wherein the determining that the post isassociated with the trigger event comprises using at least one of asentiment algorithm, a classification algorithm, a clustering algorithm,or a recommendation algorithm.
 10. The method of claim 7, wherein thedetermining that the post is associated with the trigger event is basedat least on further comprising receiving a classification of at leastone score indicative of a calculated similarity between search criteriaand the post of the scores; and updating, based at least on theclassification, the least one of the sentiment algorithm, theclassification algorithm, the clustering algorithm, or therecommendation algorithm.
 11. The method of claim 7, wherein thedetermining that the post is associated with the each of the pluralityof trigger event events comprises analyzing the post for keywordsassociated with at least one of the plurality of trigger events.
 12. Themethod of claim 11, wherein analyzing the post for keywords comprises atleast one of simple string searching, latent semantic analysis (LSA), orprobabilistic latent semantic analysis (PLSA).
 13. The method of claim7, further comprising replicating a social media stream to a database;wherein the post was obtained from the database.
 14. The method of claim13, wherein replicating the social media stream and posting the responseto the social media network comprise interacting with the social medianetwork using an application programming interface.
 15. A non-transitorycomputer-readable medium storing instructions, when executed by aprocessor, cause the processor to: monitor social media stream using asocial media portal (SMP) by receiving the social media stream and anoutput lead, wherein the social media portal is coupled to a databaseused for storing social media posts, wherein the SMP comprises at leasta stream capture engine, a database access engine, a search engine, anda scoring engine; determine that a post of the social media posts on asocial media network is associated with a trigger event by determining aplurality of scores associated with the likelihood that a plurality ofposts on the social media network are associated with the trigger event,wherein the score represents calculated similarity between searchcriteria and the social media posts, and wherein the trigger eventcomprises life events, complaint events and insurance events; route, thesocial media posts based on output of the scoring engine, wherein ahighest score represents a greatest likelihood the trigger event isassociated with a social media post; generate a lead depending on whichtrigger event is calculated to have the highest score; filter, usingclassification, clustering and recommendation machine learningalgorithms, based at least on the plurality of scores, the plurality ofsocial media posts; transmit a request for a response to the filteredplurality of social media posts; transmit, based at least on thedetermining that the social media post is associated with the triggerevent and to a computing device, the post; receive, from the computingdevice, an indication of a social media account; add the response to adata store comprising a plurality of responses to the plurality ofposts; and post, based at least on the plurality of responses beingapproved, the plurality of responses to the social media network underthe social media account.
 16. The non-transitory computer-readablemedium of claim 15, wherein the instructions, when executed by theprocessor, further cause the processor to determine the approval of theresponse based at least on an authorization of a sender of the response.17. The non-transitory computer-readable medium of claim 15, wherein theinstructions cause the processor to determine the score using at leastone of a sentiment algorithm, a classification algorithm, a clusteringalgorithm, or a recommendation algorithm.
 18. The non-transitorycomputer-readable medium of claim 15, wherein the instructions, whenexecuted by the processor, further cause the processor to monitor asocial media stream for posts comprising a search criteria; and whereinthe post was identified by monitoring the social media stream.
 19. Thenon-transitory computer-readable medium of claim 18, wherein the searchcriteria comprises at least one of a user, a location, or a keyword. 20.The non-transitory computer-readable medium of claim 18, whereinmonitoring the social media stream comprises performing audio, image, orvideo analysis.